What Data Scientists Should Know About Kubernetes

Kubernetes is the most widely used platform for managing containerized applications. It’s open source, portable and powerful. A tremendous advantage of Kubernetes is its ability to create and scale containers automatically.

Due to the meteoric rise of Kubernetes, data scientists may feel obligated to learn how to use it. Companies increasingly include knowledge of Kubernetes and resource management among the requirements for data scientist roles. As more hardware is managed by Kubernetes, tools often require data scientists to use Kubernetes to train ML models.

Although knowing Kubernetes is an asset, you shouldn’t need to be full-stack to be a data scientist. Moreover, Kubernetes isn’t a perfect fit for every workflow.

Let’s take a closer look at the advantages and disadvantages of Kubernetes as a data scientist and what you do and don’t need to know.

Why You Should Learn Kubernetes

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It is Widely Supported

All major cloud service providers support Kubernetes. Increasingly, when it comes to on-premises versus the cloud, the cloud is winning the battle. Compatibility with cloud companies is a huge benefit.

Although there are other container orchestration systems, Kubernetes has been the most widely adopted. Even previous competitors like Docker offer support for Kubernetes, e.g., Docker Kubernetes Service. 

Its popularity shows no sign of waning, with new products and use cases for Kubernetes released every day. Learning Kubernetes makes you exceptionally attractive to employers, and you will never be short of job opportunities in the future.

It Boosts Productivity

Although Kubernetes is complex, its compatibility with other technologies is highly convenient. You can use Kubernetes in tandem with tools built specifically for cloud-native software development. Many of these tools are also free, open source and simple to use. 

Using these tools can help improve the quality and complexity of your product, speed up the development process and make it easier to meet crucial deadlines.

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Becoming a Full-Stack Developer

As a data scientist who understands Kubernetes, you’ll always be in demand. Countless companies use Kubernetes, from dating apps to e-commerce platforms.

Cultivating a diverse variety of skills is great for your career prospects. Companies will see you as an excellent investment, as they will be able to cut costs on development.

Further, it’s a huge advantage to work on a project on both the development and production side. The dev and prod environments often involve two different sets of tools. Many teams must divide into two; a data science team to develop models and an Ops team to productionize them.

All that said, in some cases, this can pose significant disadvantages. Large teams often become unwieldy and are prone to communication breakdowns. While debugging, it may be more difficult to trace the source of the problem if you are unsure who on which team was responsible for the code.

Most importantly, this fragmented team structure means that no one has a bird’s eye view of the project. It makes optimization difficult. Obvious issues can be overlooked or waved away when every team member thinks resolving them isn’t within their job remit.

Therefore, theoretically, it is far better for a data scientist to oversee the whole process. But this presents even more challenges.

How Learning Kubernetes Can Hold You Back (Work Overload)

The downside of the end-to-end approach is that your responsibilities are ill-defined. You may find yourself taking on too many tasks and struggling with time management. 

If you are a fantastic project manager, you should focus on leading the team and producing the project timeline. If you are an expert in integration with other apps to create your own affiliate program, you should work with the business end of the team. Data scientists, however, should work with data.

In development, it’s often better to be a specialist rather than a jack-of-all-trades. If you pour your time and energy into infrastructure, you may neglect your skills in data science. You could find yourself stretched too thin and end up with a poorer product overall.

Plus, even though cloud providers can help ease the challenge of Kubernetes, it’s nevertheless a steep learning curve. To use compatible software most advantageously, you need to have a thorough understanding of how Kubernetes works. You may still find yourself doing a lot more coding and less data science.

But what if you could still manage production end-to-end without agonizing over infrastructure? This is where infrastructure abstraction comes in. If you can find tools that enable you to take control of the process while reducing your workload, you can experience the best of both worlds.

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Infrastructure Abstraction

Infrastructure abstraction tools allow you to organize your data and code without getting bogged down in the minutiae of containerization, parallelization, failover and so on. By abstracting your infrastructure away from higher-level applications, you can have control of the process without wasting time and resources.

Infrastructure abstraction tools like Kubeflow and Metaflow are distinct from workflow orchestrators like Argo Workflows or Airflow, though they complement each other and are intended to function together. These tools allow you to use the same code in development and production environments, simplifying the process. 

Google developed and released Kubeflow in 2020. It is a fully-fledged machine learning platform with a simplified user interface and API and integrates with Kubernetes clusters. It offers capabilities for data preparation, training and serving. Kubeflow can model serve, model monitor, host data sets, run experiments and more.

Netflix released Metaflow in 2019. It’s a framework used by Netflix’s data scientists to write infrastructure for Python. Metaflow involves less overall coding than Kubeflow. It’s fantastic for ML readability and has built-in support for parallelism, making it easy to run several steps at once.

Kubeflow and Metaflow include extensive versioning capabilities: They log each version of your workflows, making it easy to discover errors and restore the state of a previous run. They are also convenient for dependency management because they run each step of a workflow in its container.

The Bottom Line

Thousands of companies use Kubernetes in their stack flow, from audio streaming sites to platforms offering video chat for free. Having some background in Kubernetes can give you crucial context for your role and duties as a data scientist. Understanding every stage of a product’s life cycle is a huge asset. However, it’s impossible to be an expert at every task involved. Trying to do too much causes confusion, miscommunications, overwork and ultimately harms your product. Instead, use infrastructure abstraction tools to work smarter, not harder. 

The choice to add Kubernetes to your list of skills as a data scientist ultimately rests with you, the needs of your organization and where you envision your career path taking you.

 

Grace Lau

Grace Lau is the Director of Growth Content at Dialpad, an AI-powered cloud communication platform for better and easier team collaboration. She has over 10 years of experience in content writing and strategy. Currently, she is responsible for leading branded and editorial content strategies, partnering with SEO and Ops teams to build and nurture content. Grace Lau also published articles for domains such as Vyond and Tapfiliate.

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